Dynamics of plant phenome can be accurately predicted from genetic markers

Abstract

Abstract Molecular and physiological changes across crop developmental stages shape the plant phenome and render its prediction from genetic markers challenging. Here we present dynamicGP, an efficient computational approach that combines genomic prediction with dynamic mode decomposition to characterize temporal changes in the crop phenotype and to predict genotype-specific dynamics for multiple traits. Using genetic markers and data from high-throughput phenotyping of a maize multi-parent advanced generation inter-cross population, we show that dynamicGP outperforms a state-of-the-art genomic prediction approach for multiple traits. We demonstrate that the developmental dynamics of traits whose heritability varies less over time can be predicted with higher accuracy. The approach paves the way for interrogating and integrating the dynamical interactions between genotype and phenotype over crop development to improve the prediction accuracy of agronomically relevant traits.

Code and data availability

The preprint explicitly provides public availability statements for both the maize HTP phenotypic datasets (IPK DOI repository) and the authors' R implementation of the dynamicGP algorithms (GitHub).